Skip to main content
Log in

Multi-Objective Optimization in WSN: Opportunities and Challenges

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) plays a significant role in the field of surveillance, monitoring the real time applications. Regardless its strong ability to handle such tasks, it is difficult to maintain a trade-off between the conflicting goals of network lifetime, transmission delay, high coverage and packet loss. Various solutions have been proposed by the researchers to address these issues comprising the solution in real-time network scenarios. This paper delivers a brief analysis of the solutions addressing recent research problems in WSN comprising conflicting goals, i.e. multi-objective optimization (MOO) technique. Firstly, an illustration of key optimization objective in WSNs is given which constitutes existing issues such as power control, rate control ant routing. Then, an elaboration of various objective functions used in MOO with its merits and demerits is also provided. Later, existing approaches for improving optimizing metric, applications performance of existing approaches and proposed architecture have been discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Deepa, O., & Suguna, J. (2017). An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. JKSU-Computer and Information Sciences, 13, 1–12.

    Google Scholar 

  2. Zhou, Z., Xu, J., Zhang, Z., Lei, F., & Fang, W. (2017). Energy-efficient optimization for concurrent compositions of WSN services. IEEE Acess, 17, 1–15.

    Google Scholar 

  3. Na, W., & Tianhua, W. (2016). A trusted QoS routing model for wireless sensor networks. In ICCSE (pp. 627–630).

  4. More, A., & Raisinghani, V. (2017). A survey on energy efficient coverage protocols in wireless sensor networks. Journal of King Saud University – Computer and Information Sciences, 29, 428–448.

    Article  Google Scholar 

  5. Li, H., & Lin, Z. (2017). Study on location of wireless sensor network node in forest environment. ICICT, 107, 697–704.

    Google Scholar 

  6. Arasu, K., & Ganesan, R. (2018). Effective implementation of energy aware routing for wireless sensor network. PMME, 5, 1186–1193.

    Google Scholar 

  7. Migabo, M., Djouani, K., Olwal, T. O., & Kurien, A. M. (2017). A survey on energy efficient network coding for multi-hop routing in wireless sensor networks. FNC, 94, 288–294.

    Google Scholar 

  8. Ansane, A. A., & Satao, R. A. (2017). A survey on various multipath routing protocols in wireless sensor networks. CCV, 79, 610–615.

    Google Scholar 

  9. Patnai, S. (2016). Energy management in wireless sensor network using PEGASIS. ICCC, 92, 207–212.

    Google Scholar 

  10. Mahidhar, R., & Raut, A. (2017). A survey on scheduling schemes with security in wireless sensor networks. ICISP, 78, 756–762.

    Google Scholar 

  11. Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University Computer and Information Sciences, 30, 259–267.

    Article  Google Scholar 

  12. H. Sandor, P. haller and Z Gal, “Performance Analysis of Wireless Sensor Networks”, INTER-ENG, vol. 19, pp. 842–849, 2016.

  13. Kakhandki, A. L., Hublikar, S., & Kumar, P. (2018). Energy efficient selective hop selection optimization to maximize lifetime of wireless sensor network. Alexandria Engineering Journal, 57, 711–718.

    Article  Google Scholar 

  14. Lu, Y., Zhang, T., He, E., & Comşa, I. S. (2018). Self-learning-based data aggregation scheduling policy in wireless sensor networks. Journal of Sensors, 18, 1–12.

    Google Scholar 

  15. Ferrandis, T. D., Blanes, J. S., Climent, S. S., Sempere-Paya, V., & Vera-Pérez, J. (2018). Deploy&Forget wireless sensor networks for itinerant applications. Computer Standards & Interfaces, 56, 27–40.

    Article  Google Scholar 

  16. Tawalbeh, L. A., Hashish, S., & Tawalbeh, H. (2017). Quality of service requirements and challenges in generic WSN infrastructures. SCE, 109, 1116–1121.

    Google Scholar 

  17. Arora, V. K., Sharma, V., & Sachdeva, M. (2018). On QoS evaluation for ZigBee incorporated wireless sensor network (IEEE 802.15.4) using mobile sensor nodes. Journal of King Saud University Computer and Information Sciences, 13, 1–9.

    Google Scholar 

  18. Ahlwat, P., & Dave, M. (2018). An attack model based highly secure key management scheme for wireless sensor networks. ICSCC, 125, 201–207.

    Google Scholar 

  19. Padmaja, P., & Marutheswar, G. V. (2018). Energy efficient data aggregation in wireless sensor networks. PMME, 5, 388–396.

    Google Scholar 

  20. Snigth, I., & Gosain, D. (2016). Energy analysis for trajectory based sink mobility in WSN. IMCIP, 54, 118–126.

    Google Scholar 

  21. Achour, A., Deru, L., & Deprez, J. C. (2018). Mobility management for wireless sensor networks a state-of-the-art. IUPT, 52, 1101–1107.

    Google Scholar 

  22. Manikandan, S., & Chinadurai, M. (2021). Effective energy adaptive and consumption in wireless sensor network using distributed source coding and sampling techniques. Wireless Personal Communications, 118, 1393–1404.

    Article  Google Scholar 

  23. Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2016). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms and open problems. IEEE Communications Surveys & Tutorials, 111, 1–38.

    Google Scholar 

  24. Ebhota, V. C., & Srivasatva, V. M. (2021). Performance analysis of learning rate parameter on prediction of signal power loss for network optimization and better generalization. Wireless Personal Communications, 118, 1111–1128.

    Article  Google Scholar 

  25. Li, H., & Lin, Z. (2018). Study on location of wireless sensor networks in P.Padmaja and G.V.Marutheswar. PMME, 5, 388–396.

    Google Scholar 

  26. Maheshawri, M., & Karthika, R. A. (2021). A novel QoS based secure unequal clustering protocol with intrusion detection system in wireless sensor networks. Wireless Personal Communications, 118, 1535–1557.

    Article  Google Scholar 

  27. Jiang, A., & Zheng, L. (2018). An effective hybrid routing algorithm in WSN: Ant colony optimization in combination with hop count minimization. MDPI, 28, 1–17.

    Google Scholar 

  28. Kumar, R., & Venkatesh, I. (2018). SDN-based QOS-aware multipath routing mechanism using openstac. International Journal of Pure and Applied Mathematics, 118(20), 357–364.

    Google Scholar 

  29. Sendra, S., Parra, L., Lloret, J., & Khan, S. (2017). Systems and algorithms for wireless sensor networks based on animal and natural behavior. International Journal of Distributed Sensor Networks, 112, 1–19.

    Google Scholar 

  30. Umamaheshwari, S. (2021). Hybrid optimization model for energy efficient cloud assisted wireless sensor network. Wireless Personal Communications, 118, 873–885.

    Article  Google Scholar 

  31. Lavangya, N., & Shankar, T. (2017). Energy optimization in wireless sensor network using NSGA-II. ARPN, 12(23), 6698–6702.

    Google Scholar 

  32. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 1–52.

    Article  Google Scholar 

  33. Hammoudeh, M., & Newmanb, R. (2016). Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion, 113, 1–14.

    Google Scholar 

  34. Reddy, D. L., Puttamadappa, C., & Suresh, H. N. (2021). Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in wireless sensor network. Pervasive and Mobile Computing, 71, 13–38.

    Article  Google Scholar 

  35. Yahiaoui, S., Omar, M., Bouabdallah, A., Natalizio, E., & Challal, Y. (2018). An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks. International Journal of Electronics and Communications, 83, 193–203.

    Article  Google Scholar 

  36. Hao, X., Yao, N., Wang, L., & Wang, J. (2020). Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks. Applied Soft Computing, 94, 1064–1070.

    Article  Google Scholar 

  37. Magaiaa, N., Hortab, N., Nevesb, R., Pereira, P. R., & Correia, M. (2016). A multi-objective routing algorithm for wireless multimedia sensor networks. Applied Soft Computing, 145, 1–27.

    Google Scholar 

  38. Ghosal, A., Halder, S., & Das, S. K. (2020). Distributed on-demand clustering algorithm for lifetime optimization in wireless sensor networks. Journal of Parallel and Distributed Computing, 141, 129–142.

    Article  Google Scholar 

  39. Iqbal, M., Naeem, M., Anpalagan, A., Qadri, N. N., & Imran, M. (2016). Multi-objective optimization in sensor networks: Optimization classification, applications. Computer Networks, 2016, 1–30.

    Google Scholar 

  40. Phoemphon, S., So-In, C., & Leelathakul, N. (2021). Improved distance estimation with node selection localization and particle swarm optimization for obstacle-aware wireless sensor networks. Expert Systems with Applications, 175, 47–73.

    Article  Google Scholar 

  41. Jha, S. K., & Eyong, E. M. (2017). An energy optimization in wireless sensor networks by using genetic algorithm. Telecommunications Systems, 66, 31–39.

    Google Scholar 

  42. Zhang, X., Lu, X., & Zhang, X. (2020). Mobile wireless sensor network lifetime maximization by using evolutionary computing methods. Ad Hoc Networks, 101, 94–102.

    Article  Google Scholar 

  43. Li, X., Liu, A., Xie, M., Xiong, N. N., Zeng, Z., & Cai, Z. (2018). Adaptive aggregation routing to reduce delay for multi-layer wireless sensor networks. MDPI, 18, 1–28.

    Google Scholar 

  44. Younus, M. U., Khan, M. K., Anjum, M. R., Afridi, S., Arain, Z. A., & Jamali, A. A. (2021). Optimizing the lifetime of software defined wireless sensor network via reinforcement learning. IEEE Access, 9, 259–272.

    Article  Google Scholar 

  45. Zaki, M., Al, H., & Gunay, M. (2017). Lifetime maximization by partitioning approach in wireless sensor networks. Journal on Wireless Communications and Networking, 15, 1–29.

    Google Scholar 

  46. Tsoumanis, G., Oikonomou, K., Aissa, S., & Stavrakakis, I. (2021). Energy and distance optimization in rechargeable wireless sensor networks. Green Communications and Networking, 5, 378–391.

    Article  Google Scholar 

  47. Mehari, M. T., De Poorter, E., Couckuyt, I., Deschrijver, D., Vermeeren, G., Plets, D., Joseph, W., Martens, L., Dhaene, T., & Moerman, I. (2016). Efficient identification of a multi-objective pareto front on a wireless experimentation facility. IEEE Transactions on Wireless Communications, 2016, 1–13.

    Google Scholar 

  48. Kaur, T., & Kumar, D. (2021). MACO-QCR: Multi-objective ACO-based QoS-aware cross-layer routing protocols in WSN. IEEE Sensors Journal, 21, 6775–6783.

    Article  Google Scholar 

  49. Prasad, D. R., Naganjaneyulu, P. V., & Prasad, K. S. (2016). Energy efficient clustering in multi-hop wireless sensor networks using differential evolutionary MOPSO. IJETT, 59, 1–15.

    Google Scholar 

  50. Luo, C., Satpute, M. N., Li, D., Wang, Y., Chen, W., & Wu, W. (2021). Fine-grained trajectory optimization of multiple UAVs for efficient data gathering from WSNs. IEEE/ACM Transactions on Networking, 29, 162–175.

    Google Scholar 

  51. Prusty, A. R., Sethib, S., & Nayakc, A. K. (2017). Multi-objective optimality in energy efficient routing for heterogeneous wireless ad hoc sensor network with clustering. IJIDT, 11, 61–70.

    Google Scholar 

  52. Zhu, Y., Gong, S., Chi, K., Li, Y., & Fang, Y. (2021). Optimizing superframe and data buffer to achieve maximum throughput for 802.15.4-based energy harvesting wireless sensor networks. IEEE Internet of Things Journal, 8, 3689–3704.

    Article  Google Scholar 

  53. Sarkar, A., & Murugan, T. S. (2016). Routing protocols for wireless sensor networks: What the literature says? Alexandria Engineering Journal, 55, 3173–3183.

    Article  Google Scholar 

  54. Rathee, M., Kumar, S., Gandomi, A. H., Dilip, K., Balusamy, B., & Patan, R. (2021). Ant colony optimization based quality of service aware energy balancing secure routing algorithm for wireless sensor networks. IEEE Transactions on Engineering Management, 68, 170–182.

    Article  Google Scholar 

  55. Iqbal, M., Naeem, M., Anpalagan, A., Ahmed, A., & Azam, M. (2016). Wireless sensor network optimization: Multi-objective paradigm. MDPI, 16, 17573–17609.

    Google Scholar 

  56. Srinivasan, R., & Kannan, E. (2018). Energy harvesting based efficient routing scheme for wireless sensor network. Wireless Personal Communications, 101, 1457–1468.

    Article  Google Scholar 

  57. Tan, J., Liu, A., Zhao, M., Shen, H., & Ma, M. (2018). Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks. Journal on Wireless Communications and Networking, 2018, 1–26.

    Google Scholar 

  58. Yang, Q., & Yoo, S. (2018). Optimal UAV path planning: Sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IJDCN, 118, 1–4.

    Google Scholar 

  59. Xua, Y., Dinga, O., & Qub, R. (2018). Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 68, 268–282.

    Article  Google Scholar 

  60. Chun Li, S., Wang, P., & Lu, M. (2016). Jointly optimized QoS-aware virtualization and routing in software defined networks. Computer Networks, 96, 69–78.

    Article  Google Scholar 

  61. Yogarajan, G., & Revathi, T. (2017). Improved cluster based data gathering using ant lion optimization in wireless sensor networks. Wireless Personal Communications, 2017, 1–21.

    Google Scholar 

  62. Kaur, S., & Mahajan, R. (2018). Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks. Egyptian Informatics Journal, 66, 1–6.

    Google Scholar 

  63. Yang, T., Xiangyang, X., Peng, L., Tonghui, L., & Leina, P. (2018). A secure routing of wireless sensor networks based on trust evaluation model. ICICT, 131, 1156–1163.

    Google Scholar 

  64. Ramluckun, N., & Bassoo, V. (2018). Energy-efficient chain-cluster based intelligent routing technique for wireless sensor networks. Applied Computing and Informatics, 66, 1–12.

    Google Scholar 

  65. Xu, Y., Ding, O., Qu, R., & Li, K. (2018). Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 66, 1–30.

    Google Scholar 

  66. Shahzad, F., Sheltami, T. R., & Shakshuki, E. M. (2016). Multi-objective optimization for a reliable localization scheme in wireless sensor networks. Journal Communications and Networks, 18(5), 796–805.

    Article  Google Scholar 

  67. Alanis, D., Botsinis, P., Babar, Z., Nguyen, H. V., Chandra, D., Ng, S. X., Hanzo, L. (2018). Quantum-aided multi-objective routing optimization using back-tracing-aided dynamic programming. IEEE Transactions on Vehicular Technology, 2018, 1–5.

    Google Scholar 

  68. Onthachi, D., & Jayabal, S. (2017). An optimized QoS-based multipath routing protocol for wireless sensor networks. IJIES, 11(2), 49–56.

    Article  Google Scholar 

  69. Zhang, J., & Zhang, X. (2018). “A prototype”, adaptive wireless network multiobjective optimization algorithm based on image synthesis. AIES, 225, 1–23.

    Google Scholar 

  70. Alwan, H., & Agarwal, A. (2017). MQoSR: A multiobjective QoS routing protocol for wireless sensor networks. ISRN Sensor Networks, 13, 1–3.

    Google Scholar 

  71. Arya, R., & Sharma, S. C. (2016). Optimization approach for energy minimization and bandwidth estimation of WSN for data centric protocols. International Journal of System Assurance Engineering and Management, 17, 1–15.

    Google Scholar 

  72. Cao, B., Zhao, J., Yang, P., Lv, Z., Liu, X., & Min, G. (2018). 3D multi-objective deployment of an industrial wireless sensor network for maritime applications utilizing a distributed parallel algorithm. Transactions on Industrial Informatics, 66, 1–10.

    Google Scholar 

  73. Céspedes-Mota, A., Castañón, G., Martínez-Herrera, A. F., & Cárdenas-Barrón, L. E. (2018). Multiobjective optimization for a wireless ad hoc sensor distribution on shaped-bounded areas. Mathematical Problems in Engineering, 2018, 1–23.

    Article  MathSciNet  MATH  Google Scholar 

  74. Ge, Y., Wang, S., & Ma, J. (2018). Optimization on TEEN routing protocol in cognitive wireless sensor network. Journal on Wireless Communications and Networking, 2018, 1–9.

    Google Scholar 

  75. Hajizadeh, N., Jahanbazi, P., & Javidan, R. (2018). Controlled deployment in wireless sensor networks based on a novel multi objective bee swarm optimization algorithm. CSIE, 66(1–7), 2018.

    Google Scholar 

  76. Han, R., Gao, Y., & Wu, C. (2018). An effective multi-objective optimization algorithm for spectrum allocations in the cognitive-radio-based internet of things. Geneal of Latex Class File, 66, 1–10.

    Google Scholar 

  77. Kang, Z., Zeng, H., & Hu, H. (2017). Multi-objective optimized connectivity restoring of disjoint segments using mobile data collectors in wireless sensor network. EURASIP, 117, 1–22.

    Google Scholar 

  78. Khabiri, M., & Ghaffari, A. (2017). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications, 217, 1–23.

    Google Scholar 

  79. Lozano-Garzona, C., Camelob, M., Vilab, P., & Donoso, Y. (2016). A multi-objective routing algorithm for wireless mesh network in a smart cities environment. Journal of Networks, 430, 60–69.

    Google Scholar 

  80. Ma, X., Dong, H., Liu, X., Jia, L., Xie, G., & Bian, Z. (2018). An optimal communications protocol for maximizing lifetime of railway infrastructure wireless monitoring network. IEEE Transactions on Industrial Informatics, 66, 1–11.

    Google Scholar 

Download references

Acknowledgements

Authors give their gratitude to MJP Rohilkhand University, Bareilly and Netaji Subhash University of Technology, Delhi.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rashmi Chaudhry.

Ethics declarations

Conflict of interest

The authors of this paper declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, O., Rishiwal, V., Chaudhry, R. et al. Multi-Objective Optimization in WSN: Opportunities and Challenges. Wireless Pers Commun 121, 127–152 (2021). https://doi.org/10.1007/s11277-021-08627-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08627-5

Keywords

Navigation